The course covers a variety of topics in cloud computing systems, or more precisely, distributed systems that enable modern cloud computing. The topics include virtualization and its impact on system design, cloud-scale storage, cloud data processing and machine learning systems, and cloud configuration management. The course also covers the latest advancements in cloud computing/systems, IoT, edge, and fog computing. Students are expected to have background in operating systems fundamentals.

How do self-driving cars figure out what to do? In this seminar-style class, students read scientific papers and perform a research project pertaining to algorithms for planning and decision-making, with an emphasis on autonomous systems. Students outside Computer Science are welcome with permission of the instructor. Students from prior years are welcome to take the class again and either extend their previous work or choose a new topic.

Course focuses on the formal specification and verification of reactive systems, most notably concurrent and distributed systems. Topics relevant to these systems, such as non-determinism, safety and liveness properties, asynchronous communication or compositional reasoning, are discussed. We rely on a notation (T LA+, the Temporal Logic of Actions) and a support tool (TLC, the TLA+ Model Checker). Students are expected to be knowledgeable in logic and to be able to write symbolic proofs in predicate calculus. A basic understanding of the notions of assertion, precondition, and post-condition is also assumed.

Neural networks are a class of machine learning models which have recently revolutionized many applied machine learning domains such as natural language understanding, image/video processing, bioinformatics, time series analysis. This course teaches students to develop new neural network architectures from scratch and customize them. The course covers all necessary foundations of neural networks including gradient descent optimization and vector calculus. Students will learn how to design models using idioms such as observed variables, latent variables, gate variables and different functions as well as a wide range of state-of-the-art architectures as design examples. Students are expected to have background in data structures.